Open Access

ARTICLE

Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

Ming Li¹, Zhong Fang², Wanwan Cao¹, Yong Ma^1,*, Shang Wu¹, Yang Guo¹, Yu Xue³, Romany F. Mansour⁴

1 Information and Communication Branch of State Grid Anhui Electric Power Co., Ltd., Hefei, 230009, China
2 State Grid Anhui Electric Power Co., Ltd., Chuzhou Power Supply Company, Chuzhou, 239000, China
3 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
4 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: Yong Ma. Email:

Computer Systems Science and Engineering 2021, 38(1), 39-46. https://doi.org/10.32604/csse.2021.016189

Received 20 December 2020; Accepted 26 January 2021; Issue published 01 April 2021

Abstract

With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random forest and combines the Drosophila algorithm to optimize the internal parameters of the random forest, thereby improving the performance of the random forest algorithm. This method uses MapReduce to train an improved random forest model on the cloud computing platform, and then uses the trained model to analyze the residential electricity consumption data set, divides all residents into 5 categories, and verifies the effectiveness of the model through experiments and feasibility.

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Citations